Dynamic Web Content Insertion via Machine Learning
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Solution Overview
Problem
Websites face challenges in providing personalized content to users without degrading the user experience, as excessive user input for data collection can lead to increased network traffic and reduced responsiveness, especially when interacting with sponsored content.
Innovation Solution
A system for dynamic web content insertion that uses machine-learning components to adapt data collection and presentation, reducing redundant data entry by pushing known user data and dynamically generating content, and incorporating lightweight code to inject content into third-party websites, thereby optimizing user experience and interaction sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If more user inputs are collected for personalization, then customization accuracy is improved, but user experience degrades and network traffic increases
Solution Approach 1:
The system performs preliminary actions by pushing known user data to the website before the user needs to enter it. This allows the system to have user information ready in advance, reducing the need for users to re-enter data and improving personalization without degrading user experience.
Solution Approach 2:
The invention extracts only the necessary user data that is not already known to the website. Instead of collecting all possible user inputs, the system identifies and collects only the missing information, reducing network traffic and user burden while maintaining personalization accuracy.
2Measurement precision
If more user data entry interactions occur, then personalization is improved, but network traffic increases and system responsiveness decreases
Solution Approach 1:
The system pushes known user data to the website in advance, before interactions occur. This preliminary action reduces the amount of data that needs to be transmitted during user interactions, thereby improving system responsiveness while maintaining personalization accuracy.
Solution Approach 2:
The system performs partial data collection by only gathering user data that is not already known to the website. This avoids excessive data transmission and processing, maintaining system responsiveness while achieving sufficient personalization accuracy.
3Loss of time
If user data is pushed to website in advance, then redundant data entry is reduced, but data privacy concerns may increase
Solution Approach 1:
The system extracts and pushes only the specific user data that is necessary and not already known to the website. This selective data transmission reduces data privacy risks by minimizing the amount of user data shared, while still reducing redundant data entry requirements.
Data Source
AI summary
A system includes a network interface, a processing system, and a memory system. The memory system stores instructions that when executed by the processing system result in receiving a request and request data associated with a user from a web server and analyzing the request data to identify a primary offer associated with the request. A look-alike model is accessed to determine at least one secondary offer based on one or more of: the request, the request data, and the primary offer. The primary offer and the at least one secondary offer are provided for presentation to the user through a user interface.